Classification of ECG beats Using Cross Wavelet Transform and Support Vector Machines

被引：0

作者：

Jacob, Neenu ^{[1
]}

Joseph, Liza Annie ^{[1
]}

机构：

[1] Rajagiri Sch Engn & Technol, Dept Appl Elect & Instrumentat Engn, Kochi, Kerala, India

来源：

PROCEEDINGS OF THE 2015 IEEE RECENT ADVANCES IN INTELLIGENT COMPUTATIONAL SYSTEMS (RAICS) | 2015年

关键词：

Cross wavelet transform; Support Vector Machine; Heart beat classification; arrhythmia;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, heart beat classification is performed using cross wavelet transform (XWT), and support vector machines(SVM). XWT is used for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time domain signals gives a measure of similarity between two waveforms. The proposed algorithm uses XWT to analyze ECG data and determine wavelet coherence(WCOH) and wavelet cross spectrum (WCS). WCOH and WCS obtained are used mathematically to determine the parameter(s) for the purpose of classification. SVM is used to classsify the beats based on the parameters calculated from WCOH and WCS. MIT-BIH arrhythmia database is used for evaluation of results. An overall accuracy of 94.8% for SVM based classification and 96.2% for two dimensional SVM based classification was obtained using the proposed method.

引用

页码：191 / 194

页数：4

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